/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    FilteredAssociator.java
 *    Copyright (C) 2007-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.associations;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;

import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.MultiFilter;

/**
 * <!-- globalinfo-start --> Class for running an arbitrary associator on data
 * that has been passed through an arbitrary filter. Like the associator, the
 * structure of the filter is based exclusively on the training data and test
 * instances will be processed by the filter without changing their structure.
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -F &lt;filter specification&gt;
 *  Full class name of filter to use, followed
 *  by filter options.
 *  eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"
 *  (default: weka.filters.MultiFilter with
 *  weka.filters.unsupervised.attribute.ReplaceMissingValues)
 * </pre>
 * 
 * <pre>
 * -c &lt;the class index&gt;
 *  The class index.
 *  (default: -1, i.e. unset)
 * </pre>
 * 
 * <pre>
 * -W
 *  Full name of base associator.
 *  (default: weka.associations.Apriori)
 * </pre>
 * 
 * <pre>
 * Options specific to associator weka.associations.Apriori:
 * </pre>
 * 
 * <pre>
 * -N &lt;required number of rules output&gt;
 *  The required number of rules. (default = 10)
 * </pre>
 * 
 * <pre>
 * -T &lt;0=confidence | 1=lift | 2=leverage | 3=Conviction&gt;
 *  The metric type by which to rank rules. (default = confidence)
 * </pre>
 * 
 * <pre>
 * -C &lt;minimum metric score of a rule&gt;
 *  The minimum confidence of a rule. (default = 0.9)
 * </pre>
 * 
 * <pre>
 * -D &lt;delta for minimum support&gt;
 *  The delta by which the minimum support is decreased in
 *  each iteration. (default = 0.05)
 * </pre>
 * 
 * <pre>
 * -U &lt;upper bound for minimum support&gt;
 *  Upper bound for minimum support. (default = 1.0)
 * </pre>
 * 
 * <pre>
 * -M &lt;lower bound for minimum support&gt;
 *  The lower bound for the minimum support. (default = 0.1)
 * </pre>
 * 
 * <pre>
 * -S &lt;significance level&gt;
 *  If used, rules are tested for significance at
 *  the given level. Slower. (default = no significance testing)
 * </pre>
 * 
 * <pre>
 * -I
 *  If set the itemsets found are also output. (default = no)
 * </pre>
 * 
 * <pre>
 * -R
 *  Remove columns that contain all missing values (default = no)
 * </pre>
 * 
 * <pre>
 * -V
 *  Report progress iteratively. (default = no)
 * </pre>
 * 
 * <pre>
 * -A
 *  If set class association rules are mined. (default = no)
 * </pre>
 * 
 * <pre>
 * -c &lt;the class index&gt;
 *  The class index. (default = last)
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author Len Trigg (trigg@cs.waikato.ac.nz)
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision$
 */
public class FilteredAssociator extends SingleAssociatorEnhancer implements AssociationRulesProducer {

    /** for serialization */
    static final long serialVersionUID = -4523450618538717400L;

    /** The filter */
    protected Filter m_Filter;

    /** The instance structure of the filtered instances */
    protected Instances m_FilteredInstances;

    /** The class index. */
    protected int m_ClassIndex;

    /**
     * Default constructor.
     */
    public FilteredAssociator() {
        m_Associator = new Apriori();
        m_Filter = new MultiFilter();
        ((MultiFilter) m_Filter).setFilters(new Filter[] { new weka.filters.unsupervised.attribute.ReplaceMissingValues() });
        m_ClassIndex = -1;
    }

    /**
     * Returns a string describing this Associator
     * 
     * @return a description of the Associator suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String globalInfo() {
        return "Class for running an arbitrary associator on data that has been passed " + "through an arbitrary filter. Like the associator, the structure of the filter " + "is based exclusively on the training data and test instances will be processed " + "by the filter without changing their structure.";
    }

    /**
     * String describing default associator.
     * 
     * @return the default associator classname
     */
    @Override
    protected String defaultAssociatorString() {
        return Apriori.class.getName();
    }

    /**
     * Returns an enumeration describing the available options.
     * 
     * @return an enumeration of all the available options.
     */
    @Override
    public Enumeration<Option> listOptions() {
        Vector<Option> result = new Vector<Option>();

        result.addElement(new Option("\tFull class name of filter to use, followed\n" + "\tby filter options.\n" + "\teg: \"weka.filters.unsupervised.attribute.Remove -V -R 1,2\"\n" + "\t(default: weka.filters.MultiFilter with\n" + "\tweka.filters.unsupervised.attribute.ReplaceMissingValues)", "F", 1, "-F <filter specification>"));

        result.addElement(new Option("\tThe class index.\n" + "\t(default: -1, i.e. unset)", "c", 1, "-c <the class index>"));

        result.addAll(Collections.list(super.listOptions()));

        return result.elements();
    }

    /**
     * Parses a given list of options.
     * <p/>
     * 
     * <!-- options-start --> Valid options are:
     * <p/>
     * 
     * <pre>
     * -F &lt;filter specification&gt;
     *  Full class name of filter to use, followed
     *  by filter options.
     *  eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"
     *  (default: weka.filters.MultiFilter with
     *  weka.filters.unsupervised.attribute.ReplaceMissingValues)
     * </pre>
     * 
     * <pre>
     * -c &lt;the class index&gt;
     *  The class index.
     *  (default: -1, i.e. unset)
     * </pre>
     * 
     * <pre>
     * -W
     *  Full name of base associator.
     *  (default: weka.associations.Apriori)
     * </pre>
     * 
     * <pre>
     * Options specific to associator weka.associations.Apriori:
     * </pre>
     * 
     * <pre>
     * -N &lt;required number of rules output&gt;
     *  The required number of rules. (default = 10)
     * </pre>
     * 
     * <pre>
     * -T &lt;0=confidence | 1=lift | 2=leverage | 3=Conviction&gt;
     *  The metric type by which to rank rules. (default = confidence)
     * </pre>
     * 
     * <pre>
     * -C &lt;minimum metric score of a rule&gt;
     *  The minimum confidence of a rule. (default = 0.9)
     * </pre>
     * 
     * <pre>
     * -D &lt;delta for minimum support&gt;
     *  The delta by which the minimum support is decreased in
     *  each iteration. (default = 0.05)
     * </pre>
     * 
     * <pre>
     * -U &lt;upper bound for minimum support&gt;
     *  Upper bound for minimum support. (default = 1.0)
     * </pre>
     * 
     * <pre>
     * -M &lt;lower bound for minimum support&gt;
     *  The lower bound for the minimum support. (default = 0.1)
     * </pre>
     * 
     * <pre>
     * -S &lt;significance level&gt;
     *  If used, rules are tested for significance at
     *  the given level. Slower. (default = no significance testing)
     * </pre>
     * 
     * <pre>
     * -I
     *  If set the itemsets found are also output. (default = no)
     * </pre>
     * 
     * <pre>
     * -R
     *  Remove columns that contain all missing values (default = no)
     * </pre>
     * 
     * <pre>
     * -V
     *  Report progress iteratively. (default = no)
     * </pre>
     * 
     * <pre>
     * -A
     *  If set class association rules are mined. (default = no)
     * </pre>
     * 
     * <pre>
     * -c &lt;the class index&gt;
     *  The class index. (default = last)
     * </pre>
     * 
     * <!-- options-end -->
     * 
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    @Override
    public void setOptions(String[] options) throws Exception {
        String tmpStr;

        tmpStr = Utils.getOption('F', options);
        if (tmpStr.length() > 0) {
            String[] filterSpec = Utils.splitOptions(tmpStr);
            if (filterSpec.length == 0) {
                throw new IllegalArgumentException("Invalid filter specification string");
            }
            String filterName = filterSpec[0];
            filterSpec[0] = "";
            setFilter((Filter) Utils.forName(Filter.class, filterName, filterSpec));
        } else {
            setFilter(new weka.filters.supervised.attribute.Discretize());
        }

        tmpStr = Utils.getOption('c', options);
        if (tmpStr.length() > 0) {
            if (tmpStr.equalsIgnoreCase("last")) {
                setClassIndex(0);
            } else if (tmpStr.equalsIgnoreCase("first")) {
                setClassIndex(1);
            } else {
                setClassIndex(Integer.parseInt(tmpStr));
            }
        } else {
            setClassIndex(-1);
        }

        super.setOptions(options);
    }

    /**
     * Gets the current settings of the Associator.
     * 
     * @return an array of strings suitable for passing to setOptions
     */
    @Override
    public String[] getOptions() {
        Vector<String> result = new Vector<String>();

        result.add("-F");
        result.add("" + getFilterSpec());

        result.add("-c");
        result.add("" + getClassIndex());

        Collections.addAll(result, super.getOptions());

        return result.toArray(new String[result.size()]);
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String filterTipText() {
        return "The filter to be used.";
    }

    /**
     * Sets the filter
     * 
     * @param value the filter with all options set.
     */
    public void setFilter(Filter value) {
        m_Filter = value;
    }

    /**
     * Gets the filter used.
     * 
     * @return the current filter
     */
    public Filter getFilter() {
        return m_Filter;
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String classIndexTipText() {
        return "Index of the class attribute. If set to -1, the last attribute is taken as class attribute.";
    }

    /**
     * Sets the class index
     * 
     * @param value the class index
     */
    public void setClassIndex(int value) {
        m_ClassIndex = value;
    }

    /**
     * Gets the class index
     * 
     * @return the index of the class attribute
     */
    public int getClassIndex() {
        return m_ClassIndex;
    }

    /**
     * Gets the filter specification string, which contains the class name of the
     * filter and any options to the filter
     * 
     * @return the filter string.
     */
    protected String getFilterSpec() {
        Filter c = getFilter();

        if (c instanceof OptionHandler) {
            return c.getClass().getName() + " " + Utils.joinOptions(((OptionHandler) c).getOptions());
        } else {
            return c.getClass().getName();
        }
    }

    /**
     * Returns default capabilities of the associator.
     * 
     * @return the capabilities of this associator
     */
    @Override
    public Capabilities getCapabilities() {
        Capabilities result;

        if (getFilter() == null) {
            result = super.getCapabilities();
            result.disableAll();
        } else {
            result = getFilter().getCapabilities();
        }

        result.enable(Capability.NO_CLASS);

        // set dependencies
        for (Capability cap : Capability.values()) {
            result.enableDependency(cap);
        }

        return result;
    }

    /**
     * Build the associator on the filtered data.
     * 
     * @param data the training data
     * @throws Exception if the Associator could not be built successfully
     */
    @Override
    public void buildAssociations(Instances data) throws Exception {
        if (m_Associator == null) {
            throw new Exception("No base associator has been set!");
        }

        // create copy and set class-index
        data = new Instances(data);
        if (getClassIndex() == 0) {
            data.setClassIndex(data.numAttributes() - 1);
        } else {
            data.setClassIndex(getClassIndex() - 1);
        }

        if (getClassIndex() != -1) {
            // remove instances with missing class
            data.deleteWithMissingClass();
        }

        m_Filter.setInputFormat(data); // filter capabilities are checked here
        data = Filter.useFilter(data, m_Filter);

        // can associator handle the data?
        getAssociator().getCapabilities().testWithFail(data);

        m_FilteredInstances = data.stringFreeStructure();
        m_Associator.buildAssociations(data);
    }

    /**
     * Gets the list of mined association rules.
     * 
     * @return the list of association rules discovered during mining. Returns null
     *         if mining hasn't been performed yet.
     */
    @Override
    public AssociationRules getAssociationRules() {
        if (m_Associator instanceof AssociationRulesProducer) {
            AssociationRules rules = ((AssociationRulesProducer) m_Associator).getAssociationRules();

            // construct a new FilteredAssociationRules
            FilteredAssociationRules fRules = new FilteredAssociationRules(FilteredAssociator.this, m_Filter, rules);

            return fRules;
        }

        // return null if we don't wrap an association rules producer
        return null;
    }

    /**
     * Gets a list of the names of the metrics output for each rule. This list
     * should be the same (in terms of the names and order thereof) as that produced
     * by AssociationRule.getMetricNamesForRule().
     * 
     * @return an array of the names of the metrics available for each rule learned
     *         by this producer.
     */
    @Override
    public String[] getRuleMetricNames() {
        if (m_Associator instanceof AssociationRulesProducer) {
            return ((AssociationRulesProducer) m_Associator).getRuleMetricNames();
        }

        return new String[0];
    }

    /**
     * Returns true if this AssociationRulesProducer can actually produce rules.
     * Most implementing classes will always return true from this method (obviously
     * :-)). However, an implementing class that actually acts as a wrapper around
     * things that may or may not implement AssociationRulesProducer will want to
     * return false if the thing they wrap can't produce rules.
     * 
     * @return true if this producer can produce rules in its current configuration
     */
    @Override
    public boolean canProduceRules() {
        if (m_Associator instanceof AssociationRulesProducer) {
            return ((AssociationRulesProducer) m_Associator).canProduceRules();
        }

        return false;
    }

    /**
     * Output a representation of this associator
     * 
     * @return a representation of this associator
     */
    @Override
    public String toString() {
        String result;

        if (m_FilteredInstances == null) {
            result = "FilteredAssociator: No model built yet.";
        } else {
            result = "FilteredAssociator using " + getAssociatorSpec() + " on data filtered through " + getFilterSpec() + "\n\nFiltered Header\n" + m_FilteredInstances.toString() + "\n\nAssociator Model\n" + m_Associator.toString();
        }

        return result;
    }

    /**
     * Main method for running this class.
     * 
     * @param args commandline arguments, use "-h" for full list
     */
    public static void main(String[] args) {
        runAssociator(new FilteredAssociator(), args);
    }
}
